Selecting the best way to forecast income in the banking industry using data mining methods, a case study

Alireza Jafari (), Amir Aghsami () and Masoud Rabbani ()
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Alireza Jafari: University of Tehran
Amir Aghsami: University of Tehran
Masoud Rabbani: University of Tehran

OPSEARCH, 2025, vol. 62, issue 3, No 14, 1383-1422

Abstract: Abstract One of the most important topics in data science is the prediction topic. The prediction accuracy is often sought after in many articles, aiming to establish trust in the prediction results. Customer revenue prediction is deemed crucial in any industry, as awareness of future income allows for strategic development across various industry sectors. Traditionally, profits and losses were assessed retrospectively following a business event. However, today, future revenue can be predicted using data science concepts. Future revenue prediction can be achieved through the application of different types of data mining algorithms, each possessing its level of accuracy. In this article, the forecasting of bank customers’ revenue will be examined using various algorithms. To this end, different forecasting methods such as regression, decision tree, random forest, cart, etc., will be implemented, both with and without clustering, to identify the optimal approach for predicting customer revenue.

Keywords: Forecasting; Data science; Customer revenue; Clustering; Prediction (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s12597-024-00852-3

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